Various types of solid-state image sensors are in use today, including charge-coupled device (CCD) image sensors and complimentary metal-oxide semiconductor CMOS image sensors.
CMOS image sensors typically utilize an array of active pixel image sensors and a row of register of correlated double-sampling (CDS) circuits or amplifiers to sample and hold the output of a given row of pixel image sensors of the array. Each active pixel image sensor of the array of pixels typically contains a pixel amplifying device (usually a source follower).
Current CMOS image sensors have inferior image performance compared to CCD image sensors. This inferior image performance is due to inherent Fixed Pattern Noise (FPN), limited dynamic range (about 72 dB) which is reduced, in part, by FPN, and low fill factor (the ration of photo-detector areas to total of the APS pixel circuitry) which results in lower sensitivity. A digital image processor is therefore necessary in order to improve the image quality of CMOS image sensors.
Several image enhancement algorithms have been developed for digital cameras that use solid state image sensors. However, because these algorithms are developed and optimized for CCDs, they do not achieve the same image enhancement for cameras using CMOS image sensors.
A major goal of image enhancement is to process an original image in a manner such the processed image is more suitable for a desired application. Image details that are blurred or corrupted are enhanced by removal of perceived noise from the image. However, by merely removing perceived noise often leads to removal of important image information along with noise.
Most of the image enhancement techniques operate on a color interpolated version of the image. The color interpolated version is generated from a brayer pattern. Each pixel location of the image sensor has the intensity level of only one of three color components Red, Green or Blue that are used to provide respective R, G, B output channels. The brayer pattern is then color interpolated such that each pixel's, missing color components are approximated to give each missing color. However, color interpolation inherently introduces noise into the image. A major requirement of image enhancement is therefore to accurately distinguish between true image details and false image details such as noise.
There are many image enhancement algorithms that include a function to expand the dynamic range of the image. An attractive way is described in Hansoo Kim & al., “Digital Signal Processor with Efficient RGB Interpolan and Histogram Accumulation”, IEEE Trans. On Consumer Electronics, vol 44, No.4, November 1998 pp 1389-1395. In this paper, a processor controls a gamma slope to extract a set of feature data from an output image. The processor calculates then the numbers of pixels of luminance values in the bright range, in the middle range and in the dark range. Another method presented in “Adaptive Gamma Processing of the video Cameras for the expansion of the dynamic range”, IEEE Trans. on Consumer Electronics vol. 41, No. 3, August 1995, pp 555-562 makes a histogram of the luminance level in a frame and expands the dynamic range by controlling the slopes of knee compensation.
Unfortunately, prior art dynamic range compression methods do not process pixel values to suit the behaviour of the human vision in which lines and edges are generally of importance. Prior art dynamic range compression methods do not ideally compensate for changing frequency characteristics with mean luminance which can be important because the nature of the noise present in the image changes significantly with mean luminance. At low values of mean luminance there is a significant proportion of high frequency noise caused by random photon events. At higher mean luminance values the signal to noise ration improves as the proportion of random photon events decreases, making detection of high frequency component far less error prone. Hence it would be useful for a dynamic range compression method to adopt the strategy of changing from object detectors at low light levels to feature detectors in bright light. This is due to the change in frequency characteristics in visual cells in the retina which act as low pass filters at low light levels and become bandpass filters as light intensity increases.
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